Literature DB >> 17724666

Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity.

Rasmus Jansson1, Ulf Bredberg, Michael Ashton.   

Abstract

One of the drug specific parameters needed in physiologically based pharmacokinetic (PBPK) models is the tissue to plasma drug concentration ratios (K(p) values). The aim of this study was to develop an empirical method for predicting K(p) values using a preclinically determined in vivo volume of distribution, in combination with descriptors for drug lipophilicity. Pharmacokinetic data in laboratory animals for a wide range of drug compounds were collected. Obtained correlations between K(p) values for muscle and other tissues, in a training set of 49 compounds, were used to predict K(p) values for a test set of 22 compounds, based on their volume of distribution and lipophilicity. Predicted K(p) values agreed well with experimentally determined values (n = 118), especially for noneliminating tissues (r(2) = 0.81) with 72% and 87% being within a factor +/-2 and +/-3, respectively. In conclusion, we present an empirical method based on a measured volume of distribution and a drug lipophilicity descriptor, which can be used to predict tissue K(p) values with reasonable accuracy. 2007 Wiley-Liss, Inc

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Year:  2008        PMID: 17724666     DOI: 10.1002/jps.21130

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  10 in total

1.  Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound.

Authors:  Yejin Esther Yun; Cecilia A Cotton; Andrea N Edginton
Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-11-21       Impact factor: 2.745

2.  Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice.

Authors:  Prashant B Nigade; Jayasagar Gundu; K Sreedhara Pai; Kumar V S Nemmani
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-10       Impact factor: 2.441

3.  Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models.

Authors:  Prashant B Nigade; Jayasagar Gundu; K Sreedhara Pai; Kumar V S Nemmani
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2018-06       Impact factor: 2.441

4.  Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions.

Authors:  Kimberly Holt; Min Ye; Swati Nagar; Ken Korzekwa
Journal:  Drug Metab Dispos       Date:  2019-07-19       Impact factor: 3.922

5.  Simultaneous Assessment of Hepatic Transport and Metabolism Pathways with a Single Probe Using Individualized PBPK Modeling of 14CO2 Production Rate Data.

Authors:  Yoko Franchetti; Thomas D Nolin
Journal:  J Pharmacol Exp Ther       Date:  2019-08-09       Impact factor: 4.030

6.  In silico prediction of efavirenz and rifampicin drug-drug interaction considering weight and CYP2B6 phenotype.

Authors:  Dinko Rekić; Daniel Röshammar; Jackson Mukonzo; Michael Ashton
Journal:  Br J Clin Pharmacol       Date:  2011-04       Impact factor: 4.335

7.  Methods to Predict Volume of Distribution.

Authors:  Kimberly Holt; Swati Nagar; Ken Korzekwa
Journal:  Curr Pharmacol Rep       Date:  2019-06-06

8.  Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics.

Authors:  Thierry Wendling; Swati Dumitras; Kayode Ogungbenro; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-08-01       Impact factor: 2.745

9.  A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform.

Authors:  Victor Antontsev; Aditya Jagarapu; Yogesh Bundey; Hypatia Hou; Maksim Khotimchenko; Jason Walsh; Jyotika Varshney
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379

10.  Proteomics-Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight.

Authors:  Christine Wegler; Luna Prieto Garcia; Signe Klinting; Ida Robertsen; Jacek R Wiśniewski; Jøran Hjelmesaeth; Anders Åsberg; Rasmus Jansson-Löfmark; Tommy B Andersson; Per Artursson
Journal:  Clin Pharmacol Ther       Date:  2020-10-18       Impact factor: 6.875

  10 in total

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